Alternatives and similar repositories for LoRAPrune:

Users that are interested in LoRAPrune are comparing it to the libraries listed below

• ROIM1998 / APT
  [ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
  ☆39Updated 11 months ago
• zyxxmu / DSnoT
  Official Pytorch Implementation of Our Paper Accepted at ICLR 2024-- Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLM…
  ☆47Updated last year
• yxli2123 / LoSparse
  ☆51Updated last year
• htqin / IR-QLoRA
  [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…
  ☆65Updated last year
• liyunqianggyn / Awesome-LLMs-Pruning
  Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.
  ☆85Updated 4 months ago
• CASIA-IVA-Lab / FLAP
  [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
  ☆49Updated last year
• BaiTheBest / SparseLLM
  Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)
  ☆58Updated last month
• SempraETY / Pruning-via-Merging
  ☆18Updated 5 months ago
• TsinghuaC3I / SoRA
  [EMNLP 2023 Main] Sparse Low-rank Adaptation of Pre-trained Language Models
  ☆75Updated last year
• MrGGLS / BlockPruner
  A block pruning framework for LLMs.
  ☆22Updated 10 months ago
• DavidFanzz / SCMoE
  ☆22Updated 11 months ago
• haiquanlu / AlphaPruning
  [NeurIPS 2024] AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
  ☆23Updated last month
• harveyhuang18 / EMR_Merging
  [NeurIPS 2024 Spotlight] EMR-Merging: Tuning-Free High-Performance Model Merging
  ☆58Updated 2 months ago
• lliai / Awesome-LoRA-Low-Rank-Adaptation
  Awesome-Low-Rank-Adaptation
  ☆94Updated 6 months ago
• ycjing / Awesome-Model-Merging
  A curated list of Model Merging methods.
  ☆92Updated 7 months ago
• osehmathias / lisa
  LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
  ☆31Updated last year
• pixeli99 / OwLore
  Official Pytorch Implementation of "OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning" b…
  ☆31Updated 11 months ago
• QingruZhang / PLATON
  This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).
  ☆45Updated 2 years ago
• ylsung / ECoFLaP
  Code for "ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models" (ICLR 2024)
  ☆19Updated last year
• mrflogs / LoRA-Pro
  Official code for our paper, "LoRA-Pro: Are Low-Rank Adapters Properly Optimized? "
  ☆115Updated 3 weeks ago
• luuyin / OWL
  Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"
  ☆65Updated 10 months ago
• yangyifei729 / LaCo
  Official implementation for LaCo (EMNLP 2024 Findings)
  ☆16Updated 7 months ago
• UNITES-Lab / moe-quantization
  Official code for the paper "Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark"
  ☆15Updated 10 months ago
• locuslab / massive-activations
  Code accompanying the paper "Massive Activations in Large Language Models"
  ☆159Updated last year
• EnnengYang / AdaMerging
  AdaMerging: Adaptive Model Merging for Multi-Task Learning. ICLR, 2024.
  ☆78Updated 6 months ago
• UNITES-Lab / MC-SMoE
  [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"
  ☆82Updated 11 months ago
• hahnyuan / ASVD4LLM
  Activation-aware Singular Value Decomposition for Compressing Large Language Models
  ☆66Updated 6 months ago
• Lucky-Lance / Expert_Sparsity
  [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
  ☆88Updated 11 months ago
• nbasyl / DoRA
  Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"
  ☆123Updated last year
• ldery / Bonsai
  Code for "Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes"
  ☆28Updated last year